Design, Manufacturing, and Characterization of a Novel Ceramic Matrix Composite
Bibliographic record
Abstract
A novel ceramic matrix composite (CMC) system consisting of a commercially available SiC fibre, variations of electrophoretically deposited (EPD) fibre-matrix interphases, and a liquid metal melt infiltrated matrix was designed and characterised. A factorial design of experiments approach was undertaken to evaluate the deposition variables which would result in a functioning fibre-matrix interphase. A 25-2 partial factorial design matrix was selected with factors: electric potential, deposition time, surfactant, binder, and solids loading. The design matrix was replicated for four different EPD fibre-matrix interphase coating combinations: Al2O3/SiC, BN/PSZ, ZrC/ZTA, and SiC/Si3N4/SiC. Microcomposites were evaluated for tensile properties using a standard displacement controlled tensile test program. Microcomposites were tested at room temperature immediately following fabrication and following exposure to a standard atmosphere at 1000 °C for 1 h. Samples with ZrC/ZTA and SiC/Si3N4/SiC coatings demonstrated the best tensile properties in room temperature tests while samples with BN/PSZ and SiC/Si3N4/SiC coatings demonstrated the best retention of tensile properties following high temperature exposure. Subsequent SEM analysis revealed that coatings with smaller particle diameters as the inner layer of the fibre-matrix interphase coating produced more uniform coatings and the less fibre degradation due to oxidation following high temperature exposure. Additional microcomposites were fabricated for high temperature tensile testing; however, these samples were unable to bear recordable loads, an SEM examination revealed significant degradation of the matrix phase beneath the high temperature adhesive. Optical microscopy was used to evaluate coating thicknesses of coated fibre bundles prior to heat treatments. Measured coating thickness indicated that generally higher deposition times resulted in thicker coatings; however, coatings produced using 25 V electric potential were thicker than coatings produced using 12.5 V and 50 V electric potentials. This is likely due to a greater deposition efficiency factor at 25 V. FEA analysis was used to evaluate the electrical properties of an idealized version of the stationary EPD cell. This analysis showed a significant variation in the electric field along the fibre axis as well as a significant variation in electrical field between fibres in the centre of the fibre bundle and on the outer edge of the fibre bundle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".